Donor Lymphocyte Infusion Induces Long-Term Donor-Specific Cardiac Xenograft Survival through Activation of Recipient Double-Negative Regulatory T Cells
Bibliographic record
Abstract
Previous studies have shown that pretransplant donor lymphocyte infusion (DLI) can enhance xenograft survival. However, the mechanism by which DLI induces xenograft survival remains obscure. Using T cell subset-deficient mice as recipients we show that CD4+, but not CD8+, T cells are necessary to mediate the rejection of concordant cardiac xenografts. Adoptive transfer of naive CD4+ T cells induces rejection of accepted cardiac xenografts in CD4-/- mice. This rejection can be prevented by pretransplant DLI in the absence of any other treatment. Furthermore, we demonstrate that DLI activates alphabeta-TCR+CD3+CD4-CD8- double-negative (DN) regulatory T (Treg) cells in xenograft recipients, and that DLI-activated DN Treg cells can inhibit the proliferation of donor-specific xenoreactive CD4+ T cells in vitro. More importantly, adoptive transfer of DLI-activated DN Treg cells from xenograft recipients can suppress the proliferation of xenoreactive CD4+ T cells and their ability to produce IL-2 and IFN-gamma in vivo. Adoptive transfer of DLI-activated DN Treg cells also prevents CD4+ T cell-mediated cardiac xenograft rejection in an Ag-specific fashion. These data provide direct evidence that DLI can activate recipient DN Treg cells, which can induce donor-specific long-term cardiac xenograft survival by suppressing the proliferation and function of donor-specific CD4+ T cells in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".